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All-photonic artificial-neural-network processor via nonlinear optics

Physical Review applied 2024年第1期22卷 014009-014009页

作者： Jasvith Raj Basani Mikkel Heuck Dirk R. Englund Stefan Krastanov Department of Electrical and Electronics Engineering Department of Electrical and Computer Engineering Institute for Research in Electronics and Applied Physics and Department of Electrical Engineering and Computer Science Department of Electrical and Photonics Engineering College of Information and Computer Sciences

Optics and photonics have recently captured interest as a platform to accelerate linear matrix processing, otherwise a bottleneck in traditional digital electronics. In this paper we propose an all-photonic computational accelerator wherein information is encoded in the amplitudes of frequency modes stored in a single ring resonator. Interaction among these modes is enabled by nonlinear optical processes. Both the matrix multiplication and elementwise activation functions on these modes (the artificial neurons) are performed through coherent processes, enabling the direct representation of negative and complex numbers without having to pass through digital electronics, a common limitation in today’s photonic architectures. This design also has a drastically lower hardware footprint compared with today’s electronic and optical accelerators, as the entirety of the matrix multiplication happens in a single multimode resonator on chip. Our architecture is unique in providing a completely unitary, reversible mode of computation, enabling on-chip analog Hamiltonian-echo backpropagation for gradient descent and other self-learning tasks. Moreover, the computational speed increases with the power of the pumps to arbitrarily high rates, as long as the circuitry can sustain the higher optical power. Lastly, the design presented here is a less demanding version of a future room-temperature quantum computational device. Therefore, while this architecture is already viable today, direct reinvestments in it would be enabling its evolution into quantum computational hardware.

关键词： Machine learning Neuromorphic computing Nonlinear optics Photonics Artificial neural networks Deep learning Neural network simulations Optical computing

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Optimizing Fog Computing Performance: A Machine Learning-Based Task Scheduling Approach Using Fuzzy Logic and Clustering 1

Optimizing Fog Computing Performance: A Machine Learning-Bas...

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1st International Conference on Advances in computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Dbritto, Ceres Lopes, Joel Oli, Sanjay Joshi, Maulik S Singh, Aniket Rayal, Ashish St Louis United States Redmond United States Dayananda Sagar College of Engineering Department of Mathematics Bangalore560078 India Gujarat Power Engineering and Research Institute GTU Department of Humanity and Science Gujarat India Veer Madho Singh Bhandari Uttarakhand Technical University Department of Computer Science and Engineering Pithoragarh Campus India School of Applied and Life Sciences Uttaranchal University Department of Mathematics Dehradun248007 India

ISBN: (纸本)9798331518578

The ability to deliver results promptly has become crucial for analytical frameworks due to the swift increase in data volumes. Complex algorithms are needed to schedule data processing activities on distributed compute clusters efficiently. Because it is impractical to create and fine-tune a scheduling policy for every task, current systems rely on basic, generic heuristics and neglect workload peculiarities. Effective task scheduling is essential for optimising the performance of fog computing systems. Fog nodes are dynamic, heterogeneous, and resource-constrained, and traditional task scheduling techniques, which were largely created for centralised cloud settings, frequently cannot accommodate these characteristics. We provide a cutting-edge machine learning (ML) based solution that adjusts job assignment to the evolving needs of the Fog environment in order to get beyond these restrictions. Fuzzy logic, a reliable unsupervised learning method, is used with the K-Means clustering algorithm in our method to effectively group. We have used Python to model and assess our suggested method. The simulation findings validate the effectiveness of our scheduling method, demonstrating notable improvements in response time, reduced network usage, and execution time reduction in comparison to other ML and non-ML based scheduling techniques available in the Python framework. © 2025 IEEE.

关键词： Fog computing

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Qualitative behavior of a highly non-linear Cutaneous Leishmania epidemic model under convex incidence rate with real data

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Mathematical Biosciences and engineering 2024年第2期21卷 2084-2120页

作者： Alqhtani, Manal Saad, Khaled M. Zarin, Rahat Khan, Amir Hamanah, Waleed M. Department of Mathematics College of Sciences and Arts Najran University Najran Saudi Arabia Bangkok10140 Thailand Department of Mathematics and Statistics University of Swat Khyber Pakhtunkhawa Pakistan Applied Research Center for Metrology Standards and Testing King Fahd University of Petroleum and Minerals Dhahran31261 Saudi Arabia Department of Electrical Engineering College of Engineering and Physics King Fahd University for Petroleum and Minerals Dhahran Saudi Arabia

In the context of this investigation, we introduce an innovative mathematical model designed to elucidate the intricate dynamics underlying the transmission of Anthroponotic Cutaneous Leishmania. This model offers a comprehensive exploration of the qualitative characteristics associated with the transmission process. Employing the next-generation method, we deduce the threshold value R0 for this model. We rigorously explore both local and global stability conditions in the disease-free scenario, contingent upon R0 being less than unity. Furthermore, we elucidate the global stability at the disease-free equilibrium point by leveraging the Castillo-Chavez method. In contrast, at the endemic equilibrium point, we establish conditions for local and global stability, when R0 exceeds unity. To achieve global stability at the endemic equilibrium, we employ a geometric approach, a Lyapunov theory extension, incorporating a secondary additive compound matrix. Additionally, we conduct sensitivity analysis to assess the impact of various parameters on the threshold number. Employing center manifold theory, we delve into bifurcation analysis. Estimation of parameter values is carried out using least squares curve fitting techniques. Finally, we present a comprehensive discussion with graphical representation of key parameters in the concluding section of the paper. © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License.

关键词： Sensitivity analysis

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An optimized framework for implementation of smart waste collection and management system in smart cities using IoT based deep learning approach

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International Journal of Information Technology (Singapore) 2024年第8期16卷 5033-5040页

作者： William, P. Patil, Jaikumar M. Panda, Sunita Venugopal, Anita Vidyullatha, Pellakuri Kumar, Nellore Manoj Jandwani, Aman Department of Information Technology Sanjivani College of Engineering SPPU Department of Computer Science and Engineering Shri Sant Gajanan Maharaj College of Engineering SGBAU Department of Electrical Electronics and Communication Engineering GITAM School of Technology IT Unit Dhofar University Department: Computer Science and Engineering Koneru Lakshmaiah Education Foundation Department of Mathematics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Research Task

Nation’s rapid urbanization growth and economic development, waste generation has significantly increased. An important environmental concern on a global scale is waste management. In a majority of the world’s nations, including India’s metropolitan centers, organic waste management is a major problem. Therefore, it is necessary to create a productive system that will either eliminate or significantly decrease this issue. It will assist us in effectively maintaining a hygienic environment, and natural atmosphere. The implementation of smart cities requires an effective garbage collection system. In the given paper, we propose a smart waste collection and management (SWCM) system centered on the Internet of Things (IoT) architecture. A deep learning (DL) approach for classifying waste is being used. The proposed framework includes the use of smart bins for the collection of garbage, the categorization of that waste into several groups, and the management of smart waste. For this study, data from smart garbage cans were gathered. The data was normalized during preprocessing. The efficacy of the proposed system is assessed and compared with conventional as well as existing recent studies based on performance metrics such as classification accuracy (%) and power consumption (%). The results of the study reveal that the proposed approach is impactful in the smart collection and management of waste. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： And hygienic environment Deep learning Internet of things (IoT) Smart cities Smart waste

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Enhancing Mobile Networks for Urban Air Mobility Connectivity

IEEE Networking Letters

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IEEE Networking Letters 2024年第2期6卷 110-114页

作者： Piccioni, Alex Marotta, Andrea Rinaldi, Claudia Graziosi, Fabio University of l'Aquila Computer Science and Mathematics Department of Information Engineering L'Aquila67100 Italy National Inter-University Consortium for Telecommunications University of l'Aquila Research Unit Parma43124 Italy

Aerial technologies represent a fundamental part of transport systems, and the overload of terrestrial transportation as well as the high demand in urban scenarios are forcing for new solutions. From this perspective, Urban Air Mobility (UAM) is emerging as an outperforming solution to guarantee high-speed vertical transportation taking advantage of low altitudes. Connectivity is a key factor for UAM vehicles, and mobile networks represent the best solution to provide immediate connectivity with high-performance and low expenses. This calls for the development of enhancement strategies for terrestrial radio access networks. This letter aims to investigate a strategy to provide connectivity for UAM vehicles while minimizing the number of base stations required to provide UAM connectivity. Insights on the impact of different design parameters on the number of enhanced BSs required to guarantee average and minimum throughput requirements for UAM users are provided. © 2019 IEEE.

关键词： Antennas

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Resonance for analog recurrent neural network

Resonance for analog recurrent neural network

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Qu, Yurui Zhou, Ming Khoram, Erfan Yu, Nanfang Yu, Zongfu Department of Electrical and Computer Engineering University of Wisconsin MadisonWI53706 United States Department of Applied Physics and Applied Mathematics Columbia University New YorkNY10027 United States

ISBN: (纸本)9781957171258

Resonance, as a ubiquitous feature across many wave systems, is a natural candidate for analog computing in temporal signals. We demonstrate that resonance can be used to construct stable and scalable recurrent neural networks. By including resonators with different lifetimes, the computing system develops both short-term and long-term memories simultaneously. © Optica Publishing Group 2023 © 2023 The Author(s)

关键词： Recurrent neural networks

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Designing Quantum-Resilient Blockchain Frameworks: Enhancing Transactional Security with Quantum Algorithms in Decentralized Ledgers

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International Journal of Advanced computer Science and Applications 2025年第4期16卷 618-628页

作者： Kale, Meenal R. Baker El-Ebiary, Yousef A. Sathiya, L. Burugari, Vijay Kumar Yulduz, Erkiniy Muniyandy, Elangovan Alanazi, Rakan Department of Humanities Yeshwantrao Chavan College of Engineering Hingna Nagpur India Faculty of Informatics and Computing UniSZA University Malaysia Department of CSE Panimalar Engineering College Chennai India Dept of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Automatic Control and Computer Engineering Department Turin Polytechnic University in Tashkent Tashkent Uzbekistan Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan Department of Information Technology Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia

Quantum computing is progressing at a fast rate and there is a real threat that classical cryptographic methods can be compromised and therefore impact the security of blockchain networks. All of the ways used to secure blockchain like Rivest–Shamir–Adleman (RSA), Elliptic Curve Cryptography (ECC) and Secure Hash Algorithm 256-bit (SHA256) are the characteristic of the traditional cryptographic techniques vulnerable to attack by quantum algorithms: Shor’s and Grover’s algorithms: can efficiently break asymmetric encryption and speed up brute force attacks. Because of this vulnerability, there exists a need to develop an advance quantum resilient blockchain framework to protect the decentralized ledgers from the future threats of the quantum. This research proposes Post-Quantum Cryptography (PQC), Quantum Key Distribution (QKD) and Quantum Random Number Generation (QRNG) as a formidable architectural integration, to fortify security of blockchain. Classical encryption is replaced with PQC, QKD with secure key exchange by detecting eavesdropping, and QRNG with improving cryptographic randomness to remove the predictable key vulnerability. Only with a small loss of transaction efficiency, we increase transaction encryption accuracy, key exchange security, and resistance to quantum attacks. In this quantum enhanced blockchain design, the idea is to preserve the decentralization, transparency and security and at the same time overcome the future quantum threat. By going through rigorous analysis and comparative evaluation, we demonstrate that the approach saves blockchain networks from the emerging quantum risks to make sure that the decentralized finance, smart contracts and cross chain transactions. © (2025), (Science and Information Organization). All rights reserved.

关键词： Quantum cryptography

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Mathematical Modelling of Dengue Transmission with Adaptive Fuzzy Backstepping Control and RK-5 Numerical Method

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International Journal of applied and Computational mathematics 2025年第3期11卷 1-29页

作者： Vijayalakshmi, G.M. Ariyanatchi, M. Akgül, Ali Department of Mathematics Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Tamilnadu Avadi 600062 India Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Tamilnadu Chennai India Department of Mathematics Art and Science Faculty Siirt University Siirt 56100 Turkey Department of Computer Engineering Biruni University Istanbul Topkapı 34010 Turkey Department of Mathematics Mathematics Research Center Near East University Near East Boulevard Mersin 10 Nicosia 99138 Turkey

The aim of this research is to create an adaptive nonlinear fuzzy backstepping robust controller for a dengue disease outbreak model. We use an SIR-SI compartmental mathematical model to analyse the diffusion mechanism of dengue disease. A nonlinear adaptive controller regulates prevention strategies for susceptible human beings and enhances hospitalization-based treatment for the infected people. Besides, the spraying of chemical insecticides will reduce the infected mosquito population. Adaptation laws are used to derive fuzzy system parameters, enhancing the system's adaptability. Then we construct a suitable Lyapunov function, which guarantees convergence, robustness, and stability for a closed-loop system. Finally, we plot all the system's control variables graphically and calculate their numerical approximations using the Runge–Kutta technique of fifth order. These simulations demonstrate that the proposed adaptive controllers can effectively control the dengue disease and ensure the stability of the system. Hence, we conclude that adaptive backstepping control measures can protect more people from dengue infection. © The Author(s), under exclusive licence to Springer Nature India Private Limited 2025.

关键词： Adaptive back stepping Dengue model Fuzzy system Lyapunov function Runge–Kutta method

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Federated transfer learning for distributed drought stage prediction

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Discover Artificial Intelligence 2025年第1期5卷 1-13页

作者： Raza, Muhammad Owais Umar, Aqsa Rasheed, Jawad Asuroglu, Tunc Alsubai, Shtwai Department of Computer Engineering Istanbul Sabahattin Zaim University Istanbul34303 Turkey Department of Computer Science Sindh Madressatul Islam University Karachi74000 Pakistan Department of Software Engineering Istanbul Nisantasi University Istanbul34398 Turkey Applied Science Research Center Applied Science Private University Amman Jordan Faculty of Medicine and Health Technology Tampere University Tampere33720 Finland VTT Technical Research Centre of Finland Tampere33720 Finland Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj11942 Saudi Arabia

Due to the uncertain nature of drought, it is one of the most menacing natural disasters. Drought modeling (Prediction, Detection, Forecasting, and Stage Prediction) is very essential for efficient policy making. But one of the key problems with drought modeling is the limited availability of centralized datasets. To address this problem, we are a novel proposing federated learning based transfer learning models for the prediction of drought stages. In this study, satellite image dataset was collected from the Tharparkar district (prone to drought) of Pakistan. We trained the dataset using traditional and federated learning approaches, comparing centralized ML models, pre-trained models, and their respective federated learning models (FL-ResNet, FL-DenseNet, FL-MobileNet). The development of these models is the novel aspect of the study specifically for the use case of drought stage prediction. Based on the final evaluation, FL-MobileNet achieved 82% precision while baseline MobileNet scored 68%. The results show the effectiveness of novelty (federated learning), that our proposed framework improves the performance of the drought stage classification task. © The Author(s) 2025.

关键词： Federated learning

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Precision spectral line-resolution to classify greenhouse gas species in broadband mid-infrared sensing

Precision spectral line-resolution to classify greenhouse ga...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Alexis, Al Juracka, Zayna Juracka, Zakaria Khan, Amir Division of Physics Engineering Mathematics and Computer Science Optical Science Center for Applied Research Delaware State University DoverDE19901 United States

ISBN: (纸本)9781957171258

We present a novel methodology to classify greenhouse gas species by investigating the structural complexity of broadband overlapping molecular lines in the spectral region of 7.0 μm to 8.0 urn. The structural information is classified by resolving peaks and zero crossings by adaptive learning methods. © Optica Publishing Group 2023 © 2023 The Author(s)

关键词： Greenhouse gases

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